DATA INTEGRATION CORE PROJECT SUMMARY The scientific goal of the Medical University of South Carolina Transdisciplinary Collaborative Center (MUSC TCC) is to conduct translational research to understand the dynamic interaction between biological, social, psychological, behavioral, and clinical factors and health care and disease outcomes to determine the most effective ways to integrate these data into precision medicine approaches to promote health equity using allostatic load (AL) as a framework. To do this, it is essential to develop robust strategies and methods to harmonize diverse types of data on key biologic factors as well as data obtained by patient self-report and electronic health records. The Data Integration Core will create new knowledge about allostasis and estimation of allostatic load by integrating data from projects within the MUSC TCC with supplemental information about individuals and populations. Specifically, the Data Integration Core will create databases/registries that bring together the diverse types of data generated in bench experiments with clinical measurements derived from the electronic health record (EHR) and EHR data on evolution of diseases over time. This resource will be generated as part of the following specific aims: 1) Create a standards-based resource using nationally-recognized tools including REDCap and Informatics for Integrating Bench to Bedside(i2b2) for integration of data on the tumor micro environments derived from proteoglycan analyses of prostate tissue and clinical studies of impacts of glucocorticoids on pathways for vitamin D effects; 2) Integrate clinical and experimental data into longitudinal patient records to expand data sets to represent the chronological order of significant clinical and social events surrounding the timing of a critical cancer diagnosis; 3) Develop natural language processing-based tools to extract discrete details on social stressors from clinicians' notes and merge these data with clinical data within the i2b2 environment; and 4) Use data mining strategies to determine the temporal links between AL and disease risk and outcomes.